Relating self-reported subjective experience to molecular structure and receptor pharmacology, and modelling the structure-activity relationships that connect them.
An individual report is an anecdote. Aggregated across many reports and joined to each molecule's receptor-binding profile, the same material becomes analysable.
PsycheGraph represents each experience as a reified event, linked to the substances taken, to the effects described (each with an intensity, a time of onset, and a verbatim supporting quotation), and to any lasting or adverse outcomes. A parallel molecular layer records structure and receptor affinities, so reported phenomenology can be examined against receptor pharmacology rather than discussed in isolation.
The current corpus is Erowid, chosen as an openly available basis for a proof of concept. The schema is designed to accommodate more rigorously characterised sources as the work matures, including Shulgin's reports and structured psychometric instruments.
SMILES and InChI, chemical class, prodrug relationships (PubChem).
Binding affinities (Kᵢ) across serotonin, dopamine, NMDA, and opioid targets (PDSP).
Effects recorded as timed, quoted events within a hierarchical taxonomy.
Self-reported lasting effects and adverse events.
An early-stage prototype. The figures below are read from the running graph at page load.
Corpus figures withheld
Report, substance and word counts are derived from Erowid experience reports and are withheld pending a data-rights agreement. The data is retained and the analysis runs; only its publication waits. The enrichment coverage below is measured against PubChem and PDSP, which carry no such restriction.
Illustrative rather than a result. The intent is to show the form of the query.
-- Which phenomena covary with high 5-HT2A affinity (Kᵢ < 100 nM)? phenomenon mean_intensity n_substances confidence transpersonal.unity 0.81 n CI, low-n flagged perceptual.visual.geometry 0.74 n CI, low-n flagged -- joined: substance -> receptor_binding -> receptors -- substance <- experience event -> phenomenon
The evidence is self-reported, and the design treats that as a first-order constraint.
Experience reports carry well-documented biases. Reporting is subject to selection effects; drug identity and dose are typically unverified; polydrug use confounds attribution; timing is inconsistent; vocabulary is culturally primed; and outcomes are self-assessed rather than clinically measured. The response is statistical: per-statistic sample counts, confidence intervals, low-sample flags, and complete provenance, with every extracted fact linked to the quotation from which it was derived.
The approach has precedent. Ballentine, Friedman and Bzdok (Science Advances, 2022) applied natural-language processing to a corpus of Erowid reports across 40 receptor subtypes, and Zamberlan et al. (2018) reported that binding-affinity similarity tracks the semantic similarity of reports. PsycheGraph seeks to generalise that work into a reusable, provenance-tracked graph spanning multiple corpora, with the molecular join treated as first-class.
The example corpus of first-person reports. Attribution is preserved and the material is used for research.
Molecular structure: SMILES, InChI, formula, and weight.
Receptor binding affinities (UNC and NIMH PDSP).
Shulgin (PIHKAL and TIHKAL), psychometric instruments (5D-ASC, MEQ), and further corpora.
Staged and labelled: falsifiable questions first, a longer research programme behind them.
The near-term work strengthens the receptor-to-experience mapping as the corpus grows, and holds it to out-of-sample tests rather than in-sample fit. An active research programme connects the graph to mechanistic models of psychedelic action.
PsycheGraph is an independent research project. Critical feedback from researchers is welcome. I would be glad to hear from anyone who regards the method as useful or as flawed, who is open to collaboration or to validation against real data, or who can point to corpora worth incorporating.
Email [email protected]